为了研究股票价格收益率的影响因素，在上个世纪国外学者便提出了因子模型。经过了几十年的发展，如今因子模型理论已经发展的十分完备，因子模型从最初的单因子形式逐步拓展为三因子、五因子、六因子等等，大量的学者和业界人士使用不同形式的因子模型对各个国家的股市进行检验，试图解释并预测目标股票的股价收益率。 本文的研究对象是我国钢铁行业上市公司，钢铁行业是人类工业的基础、泛制造业的上游，对该行业的研究与投资仍然具有重要的意义。本文的研究目的是为了捕捉影响中国钢铁行业上市公司股票价格收益率的有效因子，分析其背后的行业逻辑和经济意义，并将该因子纳入选股策略，进行回测对比。 实证设计方面，本文将尤金法玛的五因子模型作为对照组，对ESG因子、流动性因子、动量因子和期货因子作为实验组进行检验。结合钢铁行业特征，本文将重点研究ESG因素对研究对象的影响，并进一步细分研究E(环境)、S(社会)、G(治理)三个维度各自对因变量的影响效果。此外，本文通过更换代理变量和改变样本时间区间等方式进行稳健性检验。本文使用的回归方法是Fama-MacBeth Regression中的第一步时间序列回归，使用的软件是R-Studio。 实证分析发现，ESG的因子暴露数值和显著性随着钢铁企业市值增长而增强，我国钢铁行业公司市值越大，ESG因素对其股价收益率的影响力越强；相比起S(社会)和G(治理)，E(环境)维度对钢铁行业股价收益率的影响力度最大，但对于小市值钢铁公司E的因子暴露却显著为负值；与流动性因子、动量因子和期货因子相比，ESG因子对模型解释力度的提升效果最大。在此实证的基础上，本文采用传统的多因子模型选股策略，将传统五因子模型作为对照组，对比考虑ESG因素后模拟组合的回测效果，并进一步对比分析考虑E(环境)、S(社会)和G(治理)后的各自模拟组合的回测效果。

In order to study factors influencing stock price returns, foreign scholars have proposed factor models in the last century. Nowadays, the theory of factor model is well-developed, and the factor models have gradually expanded from the original single factor form to three factors, five factors, six factors, etc. Scholars and investors use different forms of factor models to test the stock markets, trying to explain and predict the variation of stock returns. The paper is designed to capture factors which influence the stock return of China‘s steel industry. Due to the fact that manufacturing is based on the steel industry, the research and investment in the steel industry are still of significance. The paper utilizes multi-factor models to test the stock price return of target portfolios, to formulate a stock selection strategy, and finally to backtest the performance of the strategy. In terms of empirical design, this paper uses five-factor model as the control group, and tests the ESG factor, liquidity factor, momentum factor and futures factor as the experimental group. Considering the steel industry characteristics, this paper will focus more on ESG factor and divide it into environment factor, society factor, and governance factor. The regression method is the first step time series regression in Fama-MacBeth Regression, and the software used is R-Studio. The conclusion of this paper is that there is a stronger positive impact of ESG factor on the stock price return to the companies whose scale are bigger. However, the factor loading of environment factor of small-cap steel companies is significantly negative. Besides, compared with the liquidity factor, momentum factor and future factor, ESG factor has the greatest effect on improving model interpretation. Lastly, this paper will backtest the performance of different stock selection strategies. It still takes the traditional five-factor model as the control group, to compare the performance of strategies considering ESG factor, E factor, S factor and G factor respectively.